Triple
T17818852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legazpi City |
E444919
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Albay |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Albay | Statement: [Legazpi City, locatedIn, Albay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Albay Context triple: [Legazpi City, locatedIn, Albay]
-
A.
Albay
chosen
Albay is a province in the Bicol Region of the Philippines, known for the iconic Mayon Volcano and its rich Bikolano culture.
-
B.
Albay
Albay is a senior field officer rank in the Turkish Armed Forces, equivalent to the military rank of colonel in many other countries.
-
C.
Marinduque
Marinduque is an island province in the Philippines known for its heart-shaped geography and the annual Moriones Festival.
-
D.
Catanduanes
Catanduanes is an island province in the Bicol Region of the Philippines known for its rugged coastlines, surfing beaches, and predominantly Bikol-speaking population.
-
E.
Siquijor
Siquijor is a small island province in the central Philippines known for its white-sand beaches, coral reefs, and folklore surrounding mysticism and traditional healing.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e488812df081909771d51c54c405fe |
completed | April 19, 2026, 7:47 a.m. |
Created at: April 10, 2026, 10:15 a.m.